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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,172 @@ | ||
| library(boostCopula) | ||
| library(VineCopula) | ||
| library(tictoc) | ||
| logit <- function(x, inv = FALSE) { | ||
| if (!inv) { | ||
| log(x/(1-x)) | ||
| } else { | ||
| exp(x)/(1+exp(x)) | ||
| } | ||
| } | ||
| link_fun <- function(family, x) { | ||
| if (family %in% 1:2) { | ||
| sin(pi/2*tanh(x)) | ||
| } else if (family %in% c(3,13)) { | ||
| 2*logit(x, inv = T)/(1-logit(x, inv = T)) | ||
| } else if (family %in% c(23, 33)) { | ||
| -2*logit(x, inv = T)/(1-logit(x, inv = T)) | ||
| } else if (family %in% c(4,14)) { | ||
| 1/(1-logit(x, inv = T)) | ||
| } else if (family %in% c(24, 34)) { | ||
| -1/(1-logit(x, inv = T)) | ||
| } | ||
| } | ||
| # copula families | ||
| fams <- c(1, 2, 3, 13, 23, 33, 4, 14, 24, 34) | ||
| n <- 100 # number of simulation repetitions | ||
| N <- 60 # number of samples for each simulation | ||
| p <- 100 # number of variables | ||
| d <- 5 | ||
| coef_mat <- array(0, dim = c(p+1, d, d), dimnames = list(c("intercept", paste("x", sep = "", 1:p)), c(), c())) | ||
| positives_lin <- array(NA, dim = c(2, d, d), dimnames = list(c("true", "false"), c(), c())) | ||
| par_mae_lin <- array(NA, dim = c(d, d)) | ||
| fams_lin <- array(0, dim = c(d, d)) | ||
| # define 5-dimensional R-vine tree structure matrix | ||
| Matrix <- c(5, 0, 0, 0, 0, | ||
| 2, 2, 0, 0, 0, | ||
| 3, 3, 3, 0, 0, | ||
| 1, 4, 4, 4, 0, | ||
| 4, 1, 1, 1, 1) | ||
| Matrix <- matrix(Matrix, 5, 5, byrow = T) | ||
| # define R-vine pair-copula family matrix | ||
| family <- c(0, 0, 0, 0, 0, | ||
| 1, 0, 0, 0, 0, | ||
| 24, 2, 0, 0, 0, | ||
| 4, 33, 13, 0, 0, | ||
| 23, 34, 14, 3, 0) | ||
| family <- matrix(family, 5, 5, byrow = T) | ||
| # define R-vine pair-copula parameter matrix | ||
| par <- c(0, 0, 0, 0, 0, | ||
| 0.5, 0, 0, 0, 0, | ||
| -2, 0.5, 0, 0, 0, | ||
| 2, -2, 2, 0, 0, | ||
| -2, -2, 2, 2, 0) | ||
| par <- matrix(par, 5, 5, byrow = T) | ||
| # define second R-vine pair-copula parameter matrix | ||
| par2 <- c(0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, | ||
| 0, 4, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0) | ||
| par2 <- matrix(par2, 5, 5, byrow = T) | ||
| boostRVC <- RVineMatrix(Matrix = Matrix, | ||
| family = family, | ||
| par = par, par2 = par2, | ||
| names = c("U1", "U2", "U3", "U4", "U5")) | ||
| # set seed for reproducubility | ||
| set.seed(5) | ||
| x <- sapply(1:p, function(l) runif(N, -1, 1)) | ||
| colnames(x) <- paste0("x", 1:p) | ||
| u <- matrix(runif(N*d), ncol = d, dimnames = list(c(), paste0("U", 1:d, sep = ""))) | ||
| # simulate linear model | ||
| U <- boostRVineSimStudy(boostRVC, u, x, mode = "linear") | ||
| library(boostCopula) | ||
| library(VineCopula) | ||
| library(tictoc) | ||
| logit <- function(x, inv = FALSE) { | ||
| if (!inv) { | ||
| log(x/(1-x)) | ||
| } else { | ||
| exp(x)/(1+exp(x)) | ||
| } | ||
| } | ||
| link_fun <- function(family, x) { | ||
| if (family %in% 1:2) { | ||
| sin(pi/2*tanh(x)) | ||
| } else if (family %in% c(3,13)) { | ||
| 2*logit(x, inv = T)/(1-logit(x, inv = T)) | ||
| } else if (family %in% c(23, 33)) { | ||
| -2*logit(x, inv = T)/(1-logit(x, inv = T)) | ||
| } else if (family %in% c(4,14)) { | ||
| 1/(1-logit(x, inv = T)) | ||
| } else if (family %in% c(24, 34)) { | ||
| -1/(1-logit(x, inv = T)) | ||
| } | ||
| } | ||
| # copula families | ||
| fams <- c(1, 2, 3, 13, 23, 33, 4, 14, 24, 34) | ||
| n <- 100 # number of simulation repetitions | ||
| N <- 6000 # number of samples for each simulation | ||
| p <- 100 # number of variables | ||
| d <- 5 | ||
| coef_mat <- array(0, dim = c(p+1, d, d), dimnames = list(c("intercept", paste("x", sep = "", 1:p)), c(), c())) | ||
| positives_lin <- array(NA, dim = c(2, d, d), dimnames = list(c("true", "false"), c(), c())) | ||
| par_mae_lin <- array(NA, dim = c(d, d)) | ||
| fams_lin <- array(0, dim = c(d, d)) | ||
| # define 5-dimensional R-vine tree structure matrix | ||
| Matrix <- c(5, 0, 0, 0, 0, | ||
| 2, 2, 0, 0, 0, | ||
| 3, 3, 3, 0, 0, | ||
| 1, 4, 4, 4, 0, | ||
| 4, 1, 1, 1, 1) | ||
| Matrix <- matrix(Matrix, 5, 5, byrow = T) | ||
| # define R-vine pair-copula family matrix | ||
| family <- c(0, 0, 0, 0, 0, | ||
| 1, 0, 0, 0, 0, | ||
| 24, 2, 0, 0, 0, | ||
| 4, 33, 13, 0, 0, | ||
| 23, 34, 14, 3, 0) | ||
| family <- matrix(family, 5, 5, byrow = T) | ||
| # define R-vine pair-copula parameter matrix | ||
| par <- c(0, 0, 0, 0, 0, | ||
| 0.5, 0, 0, 0, 0, | ||
| -2, 0.5, 0, 0, 0, | ||
| 2, -2, 2, 0, 0, | ||
| -2, -2, 2, 2, 0) | ||
| par <- matrix(par, 5, 5, byrow = T) | ||
| # define second R-vine pair-copula parameter matrix | ||
| par2 <- c(0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, | ||
| 0, 4, 0, 0, 0, | ||
| 0, 0, 0, 0, 0, | ||
| 0, 0, 0, 0, 0) | ||
| par2 <- matrix(par2, 5, 5, byrow = T) | ||
| boostRVC <- RVineMatrix(Matrix = Matrix, | ||
| family = family, | ||
| par = par, par2 = par2, | ||
| names = c("U1", "U2", "U3", "U4", "U5")) | ||
| # set seed for reproducubility | ||
| set.seed(5) | ||
| x <- sapply(1:p, function(l) runif(N, -1, 1)) | ||
| colnames(x) <- paste0("x", 1:p) | ||
| u <- matrix(runif(N*d), ncol = d, dimnames = list(c(), paste0("U", 1:d, sep = ""))) | ||
| # simulate linear model | ||
| U <- boostRVineSimStudy(boostRVC, u, x, mode = "linear") | ||
| tic() | ||
| object <- boostRVineCopSelect(U = U, | ||
| X = x, | ||
| boostRVM = boostRVineMatrix(Matrix = Matrix, | ||
| family = family, | ||
| formula = matrix("~.", d, d, byrow = T)), | ||
| selectioncrit = "loglik", | ||
| familyset = NA, | ||
| par = FALSE, | ||
| type = "glm", | ||
| control = boost_control(mstop = 500), | ||
| cval = cval_control(cval = F), | ||
| deselect = deselect_control(deselect = T), | ||
| center = TRUE, | ||
| stabilization = "none", | ||
| na.action = na.omit, | ||
| cores = 10) | ||
| toc() | ||
| 253.249/60 | ||
| ll_true <- boostRVineTrueLL(U = U, | ||
| X = x, | ||
| boostRVM = boostRVineMatrix(Matrix = Matrix, | ||
| family = family, | ||
| formula = matrix("~.", d, d, byrow = T)), | ||
| cores = 10) | ||
| sum(unlist(ll_true), na.rm = T) | ||
| object$loglik | ||
| 100/17 |
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